Sensitivity of Extreme Hurricane Winds to Climate Change

极端飓风对气候变化的敏感性

• 批准号: 0738172
• 负责人: James Elsner
• 金额: --
• 依托单位: Florida State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2008
• 资助国家: 美国
• 起止时间: 2008-01-01 至 2010-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0738172&HistoricalAwards=false
• 关键词: Sensitivity; Extreme; Hurricane; Winds; Climate

Advances have been made in modeling extreme hurricane winds regionally. For example, the return period of a Katrina-like storm is 14 years along the entire U.S. coast as estimated from an extreme-value model derived from reliable landfall reports. But what are the return periods of hurricane winds at specific locations, like New York City? This question is more difficult to answer, because of the rarity of historical storms with sufficient intensity affecting this location. In this project extreme hurricane winds will be modeled locally based on new insights into the scaling behavior of the parameters of the extreme value distribution. This technology will allow users to condition wind exceedance probabilities on climate variables, such as ocean temperatures and steering currents, in order to quantitatively assess which cities are most sensitive to climate variations, in terms of their risk from hurricanes.The goal is to better understand how and to what extent local hurricane risk is affected by climate. The objectives are to develop and implement the technologies for anticipating extreme winds along the coast of the United States. The technical problems to be solved include (1) determining the proper model for the available data, and (2) accounting for the variable levels of uncertainty in the data records. The scientific problem is to understand how sensitive local extreme hurricane activity is to climate.A systematic approach to data modeling will be taken and the models will be made available to the scientific and risk management communities.The broader impacts of this work are a better understanding of the hurricane threat to the United States and elsewhere, a new tool kit for data modeling in the climate sciences, and estimates of hurricane return periods for any intensity at any location. Students will be educated in the theory of climate and in the application of modern statistical methods for hurricane climate research. Open-source software will be used, and students will be trained in the areas of applied computing and statistics. This work will advance and promote a broadening of quantitative data analysis that encompasses data models and Bayesian methodologies. Results will help to improve the way statistics is integrated into research and education in the climate and related sciences.

在模拟区域极端飓风方面取得了进展。例如，根据可靠的登陆报告得出的极值模型估计，卡特里娜类风暴在整个美国海岸的重现期为沿着14年。但是飓风在特定地点的重现期是多少，比如纽约市？这个问题更难回答，因为历史上很少有足够强度的风暴影响这个位置。在这个项目中，极端飓风将在本地建模的基础上，对极端值分布参数的标度行为的新见解。这项技术将使用户能够根据海洋温度和转向气流等气候变量来确定风力增强的可能性，以便从飓风风险的角度定量评估哪些城市对气候变化最敏感，目的是更好地了解当地飓风风险如何以及在多大程度上受到气候的影响。目标是开发和实施预测美国海岸沿着极端风的技术。需要解决的技术问题包括：（1）为现有数据确定适当的模型;（2）说明数据记录中各种不确定性的程度。科学问题是了解当地极端飓风活动对气候的敏感程度。将采取系统的数据建模方法，并将模型提供给科学和风险管理界。这项工作的更广泛影响是更好地了解飓风对美国和其他地方的威胁，为气候科学中的数据建模提供新的工具包，以及对任何地点任何强度的飓风重现期的估计。学生将接受气候理论和现代统计方法在飓风气候研究中的应用方面的教育。将使用开放源码软件，并对学生进行应用计算和统计方面的培训。这项工作将推动和促进扩大包括数据模型和贝叶斯方法在内的定量数据分析。研究结果将有助于改进将统计纳入气候和相关科学研究和教育的方式。